Modeling, Dynamics and Control approaches for Modern Robotics explores and investigates various theoretical and practical principles related to modeling, dynamics, and control in robotics. The objective is to enhance the understanding and development of robotic systems by applying these principles. Through accurate representations of robot kinematics and dynamics, researchers aim to effectively analyze and predict robot behavior. This title focuses on designing algorithms and control strategies for precise and efficient robotic system management.
Additionally, the book delves into sensory feedback and perception systems for robots, advancements in autonomous vehicles, industrial automation, humanoid robots, and medical robotics, showcasing the integration of technology and computing power in modern applications. The study of control approaches and the development of optimized performance schemes are highlighted, demonstrating the significance of stability and adaptive response in changing environments. This comprehensive examination underscores the evolution and complexity of robotic systems, emphasizing their growing role in various sectors.
1. 9.1 Introduction - p. 279 2. 9.2 Related works - p. 284 3. 9.3 Acceleration-based interaction force estimation - p. 288 4. 9.4 Disturbance observer parameter identification with MEMS accelerometers - p. 305 5. 9.5 High-stiffness impedance control - p. 320 6. 9.6 Discussion - p. 339 7. 9.7 Conclusion - p. 342 8. References - p. 343 9. Chapter 10. Advanced robotics: modeling, control, and applications for flexible and teleoperated flexible link robots - p. 349 10. Chapter 11. Trajectory planning of a mobile robot with obstacle avoidance using conventional methods and heuristic methods - p. 393 11. Chapter 12. Modeling and control of position servo-actuated robot manipulators - p. 411 12. Chapter 13. Developing medical robotics with AI-enhanced biosensors - p. 457 13. Chapter 14. Motion planning for coordinating unmanned vehicles in smart factories - p. 481 14. Chapter 15. Proportional-derivative control for nonlinear robot dynamics using adaptive finite-time approach - p. 533 15. Chapter 16. Simulation of the operation of an autonomous wheelchair for patients with amyotrophic lateral sclerosis with deep reinforcement learning algorithms, large language model, and tracking the patient’s iris - p. 561 16. Chapter 17. Navigation safety optimization of mobile autonomous robots using deep reinforcement learning, long short-term memory, and artificial immune system - p. 613 17. Chapter 18. Enhanced attitude control of quadcopters in hovering using fractional order controllers - p. 669 18. Chapter 19. Dynamic modeling of a class of quadrotor unmanned aerial vehicles and its control via proportional-integral-derivative and input-output feedback linearization methods - p. 705 19. Chapter 20. Investigating complex dynamics in the passive gait of the kneed bipedal robot: insights from impulsive hybrid nonlinear modeling - p. 757 20. Abstract - p. 757 21. 20.1 Introduction - p. 757 22. 20.2 Related work - p. 760 23. 20.3 The kneed bipedal robot and its dynamics - p. 763 24. 20.4 Analyzing the periodicity and complexity in the gait of the kneed bipedal robot - p. 805 25. 20.5 Conclusion and future works - p. 813 26. References - p. 813 27. Index - p. 819
Modeling, Dynamics and Control approaches for Modern Robotics explores and investigates various theoretical and practical principles related to modeling, dynamics, and control in robotics. The objective is to enhance the understanding and development of robotic systems by applying these principles. Through accurate representations of robot kinematics and dynamics, researchers aim to effectively analyze and predict robot behavior. This title focuses on designing algorithms and control strategies for precise and efficient robotic system management.
Additionally, the book delves into sensory feedback and perception systems for robots, advancements in autonomous vehicles, industrial automation, humanoid robots, and medical robotics, showcasing the integration of technology and computing power in modern applications. The study of control approaches and the development of optimized performance schemes are highlighted, demonstrating the significance of stability and adaptive response in changing environments. This comprehensive examination underscores the evolution and complexity of robotic systems, emphasizing their growing role in various sectors.
1. 9.1 Introduction - p. 279 2. 9.2 Related works - p. 284 3. 9.3 Acceleration-based interaction force estimation - p. 288 4. 9.4 Disturbance observer parameter identification with MEMS accelerometers - p. 305 5. 9.5 High-stiffness impedance control - p. 320 6. 9.6 Discussion - p. 339 7. 9.7 Conclusion - p. 342 8. References - p. 343 9. Chapter 10. Advanced robotics: modeling, control, and applications for flexible and teleoperated flexible link robots - p. 349 10. Chapter 11. Trajectory planning of a mobile robot with obstacle avoidance using conventional methods and heuristic methods - p. 393 11. Chapter 12. Modeling and control of position servo-actuated robot manipulators - p. 411 12. Chapter 13. Developing medical robotics with AI-enhanced biosensors - p. 457 13. Chapter 14. Motion planning for coordinating unmanned vehicles in smart factories - p. 481 14. Chapter 15. Proportional-derivative control for nonlinear robot dynamics using adaptive finite-time approach - p. 533 15. Chapter 16. Simulation of the operation of an autonomous wheelchair for patients with amyotrophic lateral sclerosis with deep reinforcement learning algorithms, large language model, and tracking the patient’s iris - p. 561 16. Chapter 17. Navigation safety optimization of mobile autonomous robots using deep reinforcement learning, long short-term memory, and artificial immune system - p. 613 17. Chapter 18. Enhanced attitude control of quadcopters in hovering using fractional order controllers - p. 669 18. Chapter 19. Dynamic modeling of a class of quadrotor unmanned aerial vehicles and its control via proportional-integral-derivative and input-output feedback linearization methods - p. 705 19. Chapter 20. Investigating complex dynamics in the passive gait of the kneed bipedal robot: insights from impulsive hybrid nonlinear modeling - p. 757 20. Abstract - p. 757 21. 20.1 Introduction - p. 757 22. 20.2 Related work - p. 760 23. 20.3 The kneed bipedal robot and its dynamics - p. 763 24. 20.4 Analyzing the periodicity and complexity in the gait of the kneed bipedal robot - p. 805 25. 20.5 Conclusion and future works - p. 813 26. References - p. 813 27. Index - p. 819
